A central limit theorem for mixing triangular arrays of variables whose dependence is allowed to grow with the sample size

Christian Francq; Jean Michel Zakoïan

doi:10.1017/S0266466605050577

A central limit theorem for mixing triangular arrays of variables whose dependence is allowed to grow with the sample size

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Abstract

Conditions ensuring a central limit theorem for strongly mixing triangular arrays are given. Larger samples can show longer range dependence than shorter samples. The result is obtained by constraining the rate growth of dependence as a function of the sample size, with the usual trade-off of memory and moment conditions. An application to heteroskedasticity and autocorrelation consistent estimators is proposed.

Original language	English
Pages (from-to)	1165-1171
Number of pages	7
Journal	Econometric Theory
Volume	21
Issue number	6
DOIs	https://doi.org/10.1017/S0266466605050577
Publication status	Published - 1 Dec 2005
Externally published	Yes

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title = "A central limit theorem for mixing triangular arrays of variables whose dependence is allowed to grow with the sample size",

abstract = "Conditions ensuring a central limit theorem for strongly mixing triangular arrays are given. Larger samples can show longer range dependence than shorter samples. The result is obtained by constraining the rate growth of dependence as a function of the sample size, with the usual trade-off of memory and moment conditions. An application to heteroskedasticity and autocorrelation consistent estimators is proposed.",

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AU - Zakoïan, Jean Michel

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N2 - Conditions ensuring a central limit theorem for strongly mixing triangular arrays are given. Larger samples can show longer range dependence than shorter samples. The result is obtained by constraining the rate growth of dependence as a function of the sample size, with the usual trade-off of memory and moment conditions. An application to heteroskedasticity and autocorrelation consistent estimators is proposed.

AB - Conditions ensuring a central limit theorem for strongly mixing triangular arrays are given. Larger samples can show longer range dependence than shorter samples. The result is obtained by constraining the rate growth of dependence as a function of the sample size, with the usual trade-off of memory and moment conditions. An application to heteroskedasticity and autocorrelation consistent estimators is proposed.

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DO - 10.1017/S0266466605050577

M3 - Article

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VL - 21

SP - 1165

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JO - Econometric Theory

JF - Econometric Theory

IS - 6

ER -

A central limit theorem for mixing triangular arrays of variables whose dependence is allowed to grow with the sample size

Abstract

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